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Apache Hadoop is a framework of open-source software for large-scale and storage processing on sets of data involving commodity hardware clusters.

R is a suite of software and programming language for the purpose of data visualization, statistical computations and analysis of data. It has strong graphical capabilities, and is highly extensible with object-oriented features. At its heart, R comes with a command line interpreter and is an interpreted language available for Mac, Windows and Linux machines.

If you are into predictive modelling or statistics, R offers a ton of benefits. In terms of the amount of package availabilities for applied statistics, R is basically unrivaled. R can also handle some tasks you used to need to do using other code languages. This is especially true for those who regularly use a different language to code and are using R for the first time.

Hadoop and R are a natural match and are quite complementary in terms of visualization and analytics of big data.

5 Ways Hadoop and R Work Together

There are five different ways of using Hadoop and R together:

1. Hadoop Streaming: This is a utility that lets users run and develop the Map Reduce program in languages aside from Java.

2. Hadoop Streaming: Developed by David Rosenberg, Hadoop streaming are utilities available as R scripts that make it easy to use for R users.

3. ORCH: Can be used on the non-Oracle Hadoop clusters or on the Oracle Big Data Appliance. As a matter of fact, ORCH is a Hadoop Oracle R connector.

4. RHIPE: Techniques designed for analyzing large sets of data, RHIPE stands for R and Hadoop Integrated Programming Environment.

5. RHadoop: Provided by Revolution Analytics, RHadoop is a great solution for open source hadoop and R. RHadoop is bundles with 4 primary packages of R to analyze and manage Hadoop framework data.